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ABSTRACT
Artificial Intelligence (AI) is increasingly being viewed as critical for organizational decision-making and the long-term competitiveness of firms, demanding upskilling in human-AI interaction and delegation. While trust, informed by estimated AI accuracy, is critical for such collaboration, inconsistencies between these estimates and the actual performance of AI systems often occur, potentially leading to negative outcomes. However, the effect of this inconsistency between estimated accuracy and actual performance on human-AI collaboration is not well understood in current literature. Grounded in signaling theory and expectancy violation theory, this study presents a 2 x 2 betweensubjects online experiment with the aim of examining the effects of estimated accuracy and actual performance on several dependent variables. The study's results show that while estimated accuracy strongly influences humans' cognitive trust, the inconsistency between estimated accuracy and the actual performance of AI systems leads to misplaced trust, with humans о ver-trusting low-performing AI systems or distrusting high-performing ones. Such misplaced trust reduces human-AI collaboration performance by weakening the complementarity between humans and AI. These findings contribute to current understanding of the sources and consequences of human trust in AI systems and provide practical guidance for firms wanting to improve human-AI collaborative performance.
Keywords: Decision making; Trust; Complementarity; Performance; Human-AI collaboration.
1. Introduction
AI systems are transforming how firms and their workers operate, creating collaborative environments where humans and AI systems work collaboratively to perform organizational tasks, such as decision-making (Jarrahi, 2018; Song et al., 2025; Tambe et ak, 2019). As a common form of human-AI collaboration, Al-advised decision making systems are increasingly being used by firms (Bansal et al., 2019; Cheng et al., 2023; Wilson & Daugherty, 2018), and are believed to help employees achieve better performance while demanding less cognitive resources (Daugherty & Euchner, 2020; Howard, 2019). For example, in the field of e-commerce, employees can use image recognition technology to automatically identify and label the item categories of product images, thereby improving the speed and accuracy of product management. Moreover, by analyzing user reviews with sentiment recognition technology and mining user behavior and sales data for trend prediction, employees can obtain recommendations for operational strategies and inventory management (L. Li et al., 2023). However, due to the information asymmetry between humans and AI (Hemmer et al., 2022),...